Diego Sasso Porto

Supplemental material for 'Comparative skeletal morphology of the bees (Hymenoptera, Apoidea) : a phylogenetic perspective (Bulletin of the American Museum of Natural History, no. 482)' - https://hdl.handle.net/2246/7558

Advances in bee systematics have enhanced our understanding of the phylogenetic relationships among families, subfamilies, and tribes. However, a comprehensive morphological treatment was still lacking in light of current phylogenetic knowledge. In this work, we present the most extensive comparative analysis of the adult external and internal skeletal anatomy of bees, resulting in the largest morphological phylogenetic character matrix to date. We sampled 116 bee species representing all families and subfamilies, 64 tribes (89%), and 111 (19%) genera, along with eight species of apoid wasp outgroups. Our comparative study of bee skeletal anatomy includes 394 characters—11% of which are entirely new and 68% reinterpreted from previous literature, covering both external (73% of characters) and internal (27%) anatomy. We analyzed this phylogenetic dataset using Bayesian and implied weights parsimony inferences, partitioning the data to account for among-character rate heterogeneity. While confirming the monophyly of all seven bee families, our results reveal exceptionally high evolutionary rates in the mouthparts during the transition to long-tongued bees and in the mesosoma of corbiculate lineages, providing new insights into the evolutionary dynamics of the bee phenome. We also revisited a persistent conflict regarding the earliest divergences among extant lineages, in which morphological evidence supports the Colletidae + Stenotritidae clade as the sister of all remaining bees rather than the Melittidae. By interpreting character-state transformations across the entire skeletal anatomy, we offer an essential, updated reference of synapomorphies for the seven families and include a detailed discussion of major patterns in morphological evolution.

Diversity can be examined and interpreted from various perspectives, including species richness, genetic and phenotypic differences, variation in behaviors and natural history, and phylogenetic history. Centuries of taxonomic research have revealed approximately 21,000 bee species worldwide. These can be subdivided into a hierarchy of subgroups that reflects their evolutionary history, thanks to the increasingly more comprehensive phylogenetic hypotheses available. Advances in bee systematics have enhanced our understanding of how their diversity has evolved, including their origin in the Cretaceous, shifts in their geographical distribution, the evolution of social and parasitic behaviors, and changes in relationships between bees and the plants they visit throughout a 120-million-year shared evolutionary history. An important outcome of the enduring relationship with flowering plants is the vital role bees play in pollination in both natural and agricultural ecosystems. Habitat loss, climate change, and other anthropogenic environmental alterations have led to declines in bee populations, which have sparked concerns about bee conservation and highlighted the importance of understanding the complementary aspects of diversity, including the evolutionary and geographical components of this variation. The availability of increasingly reliable and comprehensive phylogenetic hypotheses has led to significant advancements, enabling assessments of the phylogenetic diversity of bee communities and predictions regarding their vulnerability to habitat change and their ecological functions. This review explores perspectives of documenting and interpreting bee diversity in a changing world and summarizes the current bee classification while discussing the phylogenetic advances in contemporary research.

Hymenoptera is a megadiverse insect order, yet the drivers of its massive radiation remain poorly understood. Here, we employ novel comparative phylogenetic methods that integrate computational ontologies with ancestral state reconstructions of 346 discrete morphological characters to investigate the tempo and mode of phenome evolution in Hymenoptera across both adults and larvae. We examine the interplay between phenotypic rates, selection, modularity, and lineage diversification over deep time. Our analyses reveal that both larval and adult phenomes experienced an early burst scenario during the Late Permian – Triassic. However, their evolution appears decoupled, which enhanced the overall evolvability of Hymenoptera. Moreover, major phenotypic transitions in the order are complex, multifaceted processes driven by directional selection. One such event—the emergence of the wasp waist— involved a fundamental reorganization of the adult phenome. This dramatic transformation shifted net diversification from negative to positive during the Triassic, thereby enabling the subsequent survival and explosive diversification of the apocritan lineage.

The Phenoscape project has developed ontology-based tools and a knowledge base that enables the integration and discovery of phenotypes across species from the scientific literature. The Phenoscape TraitFest 2023 event aimed to promote innovative applications that adopt the capabilities supported by the data in the Phenoscape Knowledgebase and its corresponding semantics-enabled tools, algorithms and infrastructure. The event brought together 26 participants, including domain experts in biodiversity informatics, taxonomy and phylogenetics and software developers from various life-sciences programming toolkits and phylogenetic software projects, for an intense four-day collaborative software coding event. The event was designed as a hands-on workshop, based on the Open Space Technology methodology, in which participants self-organise into subgroups to collaboratively plan and work on their shared research interests. We describe how the workshop was organised, the projects developed and outcomes resulting from the workshop, as well as the challenges in bringing together a diverse group of participants to engage productively in a collaborative environment.

Reconstructing ancestral states for discrete characters is essential for understanding trait evolution in organisms. However, most existing methods are limited to individual characters and often overlook the hierarchical and interactive nature of traits. Recent advances in phylogenetics now offer the possibility of integrating knowledge from anatomy ontologies to reconstruct multiple discrete character histories. Nonetheless, practical applications that fully harness the potential of these new approaches are still lacking. This paper introduces ontophylo, an R package that extends the PARAMO pipeline to address these limitations. Ontophylo enables the reconstruction of phenotypic entities composed of amalgamated characters, such as anatomical regions or entire phenomes. It offers three new applications: (1) reconstruction of evolutionary rates of amalgamated characters using phylogenetic non‐homogeneous Poisson process (pNHPP) that allows modelling rate variation across tree branches and time; (2) reconstruction of morphospace dynamics; and (3) visualization of evolutionary rates on vector images of organisms. Ontophylo incorporates ontological knowledge to facilitate these applications. Benchmarking confirms the accuracy of pNHPP in estimating character rates under different evolutionary scenarios, and example applications demonstrate the utility of ontophylo in studying morphological evolution in Hymenoptera using simulated data. Ontophylo can be easily integrated with other ontology‐oriented and general‐purpose R packages and offers new opportunities to examine morphological evolution on a phenomic scale using new and legacy data.

Organismal anatomy is a hierarchical system of anatomical entities often imposing dependencies among multiple morphological characters. Ontologies provide a formal and computable framework for incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. They also offer new opportunities for working with semantic representations of morphological data. In this work, we present a new R package—rphenoscate—that enables incorporating ontological knowledge in evolutionary analyses and exploring semantic patterns of morphological data. In conjunction with rphenoscape, it allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the package functionality with data sets from bees and fishes. We demonstrate that ontologies can be employed to automatically set up evolutionary models accounting for trait dependencies in stochastic character mapping. We also demonstrate how ontology annotations can be explored to interrogate patterns of morphological evolution. Finally, we demonstrate that synthetic character matrices assembled from semantic phenotypes retain most of the phylogenetic information from their original data sets. Ontologies will become important tools for integrating anatomical knowledge into phylogenetic methods and making morphological data FAIR compliant—a critical step of the ongoing ‘phenomics’ revolution. Our new package offers key advancements towards this goal.

Taxonomic descriptions hold immense phenotypic data, but their natural language (NL) format poses challenges for computer analysis. In this talk, we will present Phenoscript, a user-friendly computer language enabling computer-readable species descriptions and automated phenotype comparisons, in accordance with FAIR (Findable, Accessible, Interoperable, Reusable) principles. Phenoscript facilitates the creation of semantic species descriptions that represent a knowledge graph composed of terms from predefined biological ontologies. A Phenoscript description resembles a NL description, but follows a specific language grammar. We have developed the Phenospy package: a Python-based Phenoscript toolkit. Phenospy converts Phenoscript descriptions into both NL format, facilitating scientific publication, and the Web Ontology Language (OWL) format, enabling downstream analysis and computable phenotypic comparisons. OWL is a standard for sharing semantic data on the Web. While initially designed for phenotypes, Phenoscript can be extended to create semantic ecological data, encompassing environmental traits, functional traits, and species interactions. We will discuss the integration of species and ecological traits encoded in Phenoscript into downstream analysis, highlighting its potential for phenomic-level research in biology.

1. Reconstructing ancestral states for discrete characters is essential for understanding trait evolution in organisms. However, most existing methods are limited to individual characters and often overlook the hierarchical and interactive nature of traits. Recent advances in phylogenetics now offer the possibility of integrating knowledge from anatomy ontologies to reconstruct multiple discrete character histories. However, practical applications that fully harness the potential of these new approaches are still lacking. 2. This paper introduces ontophylo , an R package that extends the PARAMO pipeline to address these limitations. Ontophylo enables the reconstruction of phenotypic entities composed of amalgamated characters, such as entire phenomes or anatomical regions. It offers three new applications: (1) reconstructing evolutionary rates of amalgamated characters using phylogenetic non-homogeneous Poisson process (pNHPP), allowing for rate variation and shifts over time and phylogeny; (2) explicit reconstruction of morphospace dynamics; and (3) direct visualization of evolutionary rates and statistics on vector images of organisms. Ontophylo incorporates ontological knowledge to facilitate these applications. 3. Benchmarking confirms the accuracy of pNHPP in estimating character rates under different evolutionary scenarios, and example applications demonstrate the utility of ontophylo in studying morphological evolution in Hymenoptera using simulated data. 4. Ontophylo is easily integrated with other ontologyoriented and general-purpose R packages and offers new opportunities to examine morphological evolution on a phenomic level using new and legacy data.